# ParaDiGMS: Parallel Diffusion Generative Model Sampler This repository contains code for the paper: [Parallel Sampling of Diffusion Models](https://arxiv.org/abs/2305.16317) \ by Andy Shih, Suneel Belkhale, Stefano Ermon, Dorsa Sadigh, Nima Anari ----- ## Update ParaDiGMS has been integrated into Huggingface Diffusers! 🥳🎉 ``` pip install diffusers==0.19.3 ``` ```python import torch from diffusers import DDPMParallelScheduler from diffusers import StableDiffusionParadigmsPipeline scheduler = DDPMParallelScheduler.from_pretrained("runwayml/stable-diffusion-v1-5", subfolder="scheduler", timestep_spacing="trailing") pipe = StableDiffusionParadigmsPipeline.from_pretrained( "runwayml/stable-diffusion-v1-5", scheduler=scheduler, torch_dtype=torch.float16 ) pipe = pipe.to("cuda") ngpu, batch_per_device = torch.cuda.device_count(), 5 pipe.wrapped_unet = torch.nn.DataParallel(pipe.unet, device_ids=[d for d in range(ngpu)]) prompt = "a photo of an astronaut riding a horse on mars" image = pipe(prompt, parallel=ngpu * batch_per_device, num_inference_steps=1000).images[0] image.save("image.png") ``` For best performance, use the improved parallel execution script implemented using torch multiprocessing. ``` python main_mp.py ``` ----- ## About ParaDiGMs accelerates sampling of diffusion models without sacrificing quality by running denoising steps in parallel. ParaDiGMs is most useful when sampling with a large number of denoising steps on multiple GPUs, giving a 2-4x wallclock speedup.  ## Animation of Algorithm  ## Sample Images  ## Speedup of 1000-step DDPM on A100  ## Results on Image and Robotics Diffusion Models  ## Citation If you find our work useful, consider citing: ``` @misc{shih2023paradigms, title = {Parallel Sampling of Diffusion Models}, author={Shih, Andy and Belkhale, Suneel and Ermon, Stefano and Sadigh, Dorsa and Anari, Nima}, publisher = {arXiv}, year = {2023}, } ```